Automated program repair (APR) has achieved promising results, especially using neural networks. Yet, the overwhelming majority of patches produced by APR tools are confined to one single location. When looking at the patches produced with neural repair, most of them fail to compile, while a few uncompilable ones go in the right direction. In both cases, the fundamental problem is to ignore the potential of partial patches. In this paper, we propose an iterative program repair paradigm called ITER founded on the concept of improving partial patches until they become plausible and correct. First, ITER iteratively improves partial single-location patches by fixing compilation errors and further refining the previously generated code. Second, ITER iteratively improves partial patches to construct multi-location patches, with fault localization re-execution. ITER is implemented for Java based on battle-proven deep neural networks and code representation. ITER is evaluated on 476 bugs from 10 open-source projects in Defects4J 2.0. ITER succeeds in repairing 76 of them, including 18 multi-location bugs which is a new frontier in the field.
Wed 17 AprDisplayed time zone: Lisbon change
16:00 - 17:30 | |||
16:00 15mTalk | RUNNER: Responsible UNfair NEuron Repair for Enhancing Deep Neural Network Fairness Research Track Li Tianlin Nanyang Technological University, Yue Cao Nanyang Technological University, Jian Zhang Nanyang Technological University, Shiqian Zhao Nanyang Technological University, Yihao Huang East China Normal University, Aishan Liu Beihang University; Institute of Dataspace, Qing Guo IHPC and CFAR at A*STAR, Singapore, Yang Liu Nanyang Technological University | ||
16:15 15mTalk | ITER: Iterative Neural Repair for Multi-Location Patches Research Track | ||
16:30 15mTalk | Out of Context: How important is Local Context in Neural Program Repair? Research Track | ||
16:45 15mTalk | Out of Sight, Out of Mind: Better Automatic Vulnerability Repair by Broadening Input Ranges and Sources Research Track Xin Zhou Singapore Management University, Singapore, Kisub Kim Singapore Management University, Singapore, Bowen Xu North Carolina State University, DongGyun Han Royal Holloway, University of London, David Lo Singapore Management University | ||
17:00 15mTalk | Strengthening Supply Chain Security with Fine-grained Safe Patch Identification Research Track Luo Changhua The Chinese University of Hong Kong, Wei Meng Chinese University of Hong Kong, Shuai Wang The Hong Kong University of Science and Technology | ||